WO2018184963A3 - Direct vehicle detection as 3d bounding boxes using neural network image processing - Google Patents
Direct vehicle detection as 3d bounding boxes using neural network image processing Download PDFInfo
- Publication number
- WO2018184963A3 WO2018184963A3 PCT/EP2018/058033 EP2018058033W WO2018184963A3 WO 2018184963 A3 WO2018184963 A3 WO 2018184963A3 EP 2018058033 W EP2018058033 W EP 2018058033W WO 2018184963 A3 WO2018184963 A3 WO 2018184963A3
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- neural network
- image processing
- vehicle detection
- bounding boxes
- input image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Ceased
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/64—Three-dimensional objects
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R1/00—Optical viewing arrangements; Real-time viewing arrangements for drivers or passengers using optical image capturing systems, e.g. cameras or video systems specially adapted for use in or on vehicles
- B60R1/20—Real-time viewing arrangements for drivers or passengers using optical image capturing systems, e.g. cameras or video systems specially adapted for use in or on vehicles
- B60R1/22—Real-time viewing arrangements for drivers or passengers using optical image capturing systems, e.g. cameras or video systems specially adapted for use in or on vehicles for viewing an area outside the vehicle, e.g. the exterior of the vehicle
- B60R1/23—Real-time viewing arrangements for drivers or passengers using optical image capturing systems, e.g. cameras or video systems specially adapted for use in or on vehicles for viewing an area outside the vehicle, e.g. the exterior of the vehicle with a predetermined field of view
- B60R1/24—Real-time viewing arrangements for drivers or passengers using optical image capturing systems, e.g. cameras or video systems specially adapted for use in or on vehicles for viewing an area outside the vehicle, e.g. the exterior of the vehicle with a predetermined field of view in front of the vehicle
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W10/00—Conjoint control of vehicle sub-units of different type or different function
- B60W10/18—Conjoint control of vehicle sub-units of different type or different function including control of braking systems
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W10/00—Conjoint control of vehicle sub-units of different type or different function
- B60W10/20—Conjoint control of vehicle sub-units of different type or different function including control of steering systems
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/14—Adaptive cruise control
- B60W30/143—Speed control
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/18—Propelling the vehicle
- B60W30/18009—Propelling the vehicle related to particular drive situations
- B60W30/18163—Lane change; Overtaking manoeuvres
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/217—Validation; Performance evaluation; Active pattern learning techniques
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/09—Supervised learning
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/091—Active learning
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
- G06V10/235—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition based on user input or interaction
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
- G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
- G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
- G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
- G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R2300/00—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle
- B60R2300/10—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle characterised by the type of camera system used
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R2300/00—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle
- B60R2300/20—Details of viewing arrangements using cameras and displays, specially adapted for use in a vehicle characterised by the type of display used
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/146—Display means
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/403—Image sensing, e.g. optical camera
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Mechanical Engineering (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Transportation (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Automation & Control Theory (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Biodiversity & Conservation Biology (AREA)
- Human Computer Interaction (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Image Analysis (AREA)
- Traffic Control Systems (AREA)
Abstract
Systems and methods of detecting and tracking one or more vehicles in a field of view of an imaging system using neural network processing. An electronic controller receives an input image from a camera mounted on the host vehicle. The electronic controller applies a neural network configured to output a definition of a three-dimensional bounding box based at least in part on the input image. The three-dimensional bounding box indicates a size and a position of a detected vehicle in a field of view of the input image. The three-dimensional bounding box includes a first quadrilateral shape outlining a rear or front of the detected vehicle and a second quadrilateral shape outline a side of the detected vehicle.
Priority Applications (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| EP18714245.0A EP3607489B1 (en) | 2017-04-04 | 2018-03-29 | Direct vehicle detection as 3d bounding boxes using neural network image processing |
| US16/500,155 US11216673B2 (en) | 2017-04-04 | 2018-03-29 | Direct vehicle detection as 3D bounding boxes using neural network image processing |
| CN201880036861.1A CN110678872A (en) | 2017-04-04 | 2018-03-29 | Direct vehicle detection as a 3D bounding box by using neural network image processing |
| KR1020197029041A KR102629651B1 (en) | 2017-04-04 | 2018-03-29 | Direct vehicle detection with 3D bounding boxes using neural network image processing |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201762481346P | 2017-04-04 | 2017-04-04 | |
| US62/481,346 | 2017-04-04 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| WO2018184963A2 WO2018184963A2 (en) | 2018-10-11 |
| WO2018184963A3 true WO2018184963A3 (en) | 2018-12-20 |
Family
ID=61827750
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/EP2018/058033 Ceased WO2018184963A2 (en) | 2017-04-04 | 2018-03-29 | Direct vehicle detection as 3d bounding boxes using neural network image processing |
Country Status (5)
| Country | Link |
|---|---|
| US (1) | US11216673B2 (en) |
| EP (1) | EP3607489B1 (en) |
| KR (1) | KR102629651B1 (en) |
| CN (1) | CN110678872A (en) |
| WO (1) | WO2018184963A2 (en) |
Families Citing this family (37)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102018206751A1 (en) * | 2018-05-02 | 2019-11-07 | Continental Automotive Gmbh | CONTOUR RECOGNITION OF A VEHICLE BASED ON MEASUREMENT DATA OF A UMFELDSENSORIK |
| US11176415B2 (en) * | 2018-05-09 | 2021-11-16 | Figure Eight Technologies, Inc. | Assisted image annotation |
| US12277406B2 (en) | 2018-08-10 | 2025-04-15 | Nvidia Corporation | Automatic dataset creation using software tags |
| US11816585B2 (en) * | 2018-12-03 | 2023-11-14 | Tesla, Inc. | Machine learning models operating at different frequencies for autonomous vehicles |
| JP2022034086A (en) * | 2018-12-07 | 2022-03-03 | ソニーセミコンダクタソリューションズ株式会社 | Information processing apparatus, information processing method, and program |
| US11494979B2 (en) | 2019-01-04 | 2022-11-08 | Qualcomm Incorporated | Bounding box estimation and lane vehicle association |
| CN113811886B (en) * | 2019-03-11 | 2024-03-19 | 辉达公司 | Intersection detection and classification in autonomous machine applications |
| DE102019204139A1 (en) | 2019-03-26 | 2020-10-01 | Robert Bosch Gmbh | Training for artificial neural networks with better utilization of the learning data sets |
| EP3716137A1 (en) * | 2019-03-27 | 2020-09-30 | Visteon Global Technologies, Inc. | Systems and methods for estimating the position of a target vehicle |
| US11823460B2 (en) * | 2019-06-14 | 2023-11-21 | Tusimple, Inc. | Image fusion for autonomous vehicle operation |
| US20210027546A1 (en) * | 2019-07-22 | 2021-01-28 | Scale AI, Inc. | Techniques for labeling cuboids in point cloud data |
| US20220262142A1 (en) * | 2019-08-14 | 2022-08-18 | Intel Corporation | Automatic generation of 3d bounding boxes from multi-camera 2d image data |
| US12347216B2 (en) * | 2019-09-12 | 2025-07-01 | Koninklijke Philips N.V. | Interactive endoscopy for intraoperative virtual annotation in vats and minimally invasive surgery |
| JP7139300B2 (en) * | 2019-10-03 | 2022-09-20 | 本田技研工業株式会社 | Recognition device, recognition method, and program |
| KR102871465B1 (en) * | 2020-01-02 | 2025-10-16 | 엘지전자 주식회사 | Enhancing performance of local device |
| DE102020103741A1 (en) * | 2020-02-13 | 2021-08-19 | Car.Software Estonia As | Method for spatial characterization of at least one vehicle image |
| JP7495178B2 (en) * | 2020-04-14 | 2024-06-04 | 株式会社Subaru | Vehicle driving support device |
| KR102270198B1 (en) * | 2020-06-08 | 2021-06-28 | 주식회사 에스아이에이 | Method for object detection based on anchor-free rpn |
| US11798210B2 (en) * | 2020-12-09 | 2023-10-24 | Salesforce, Inc. | Neural network based detection of image space suitable for overlaying media content |
| CN112633258B (en) * | 2021-03-05 | 2021-05-25 | 天津所托瑞安汽车科技有限公司 | Target determination method and device, electronic equipment and computer readable storage medium |
| RU2767831C1 (en) * | 2021-03-26 | 2022-03-22 | Общество с ограниченной ответственностью "Яндекс Беспилотные Технологии" | Methods and electronic devices for detecting objects in the environment of an unmanned vehicle |
| EP4102466A4 (en) * | 2021-04-26 | 2023-05-17 | Beijing Baidu Netcom Science Technology Co., Ltd. | OBJECT COLLISION DETECTION METHOD AND APPARATUS, DEVICE, AND STORAGE MEDIA |
| DE102021205094A1 (en) | 2021-05-19 | 2022-11-24 | Robert Bosch Gesellschaft mit beschränkter Haftung | Quality check of training data for image classifiers |
| DE102021205271A1 (en) | 2021-05-21 | 2022-11-24 | Robert Bosch Gesellschaft mit beschränkter Haftung | Quality check of training data for classification models for semantic segmentation of images |
| CN113435318B (en) * | 2021-06-25 | 2025-02-25 | 上海商汤临港智能科技有限公司 | Neural network training, image detection, driving control method and device |
| DE102021118065A1 (en) | 2021-07-13 | 2023-01-19 | Connaught Electronics Ltd. | Method for generating three-dimensional information in a three-dimensional environment, computer program product, computer-readable storage medium and assistance system |
| US12043278B2 (en) * | 2021-07-23 | 2024-07-23 | Rivian Ip Holdings, Llc | Systems and methods for determining drivable space |
| US11663807B2 (en) | 2021-08-05 | 2023-05-30 | Ford Global Technologies, Llc | Systems and methods for image based perception |
| EP4131174A1 (en) * | 2021-08-05 | 2023-02-08 | Argo AI, LLC | Systems and methods for image based perception |
| US11966452B2 (en) | 2021-08-05 | 2024-04-23 | Ford Global Technologies, Llc | Systems and methods for image based perception |
| US12304515B2 (en) | 2022-06-14 | 2025-05-20 | Ford Global Technologies, Llc | Vehicle path adjustment |
| WO2023245635A1 (en) * | 2022-06-24 | 2023-12-28 | Intel Corporation | Apparatus and method for object detection |
| US12499657B2 (en) * | 2022-09-26 | 2025-12-16 | Micron Technology, Inc. | Video stream augmentation using a deep learning device |
| JP2024109318A (en) * | 2023-02-01 | 2024-08-14 | トヨタ自動車株式会社 | Driving Support Devices |
| EP4411670A1 (en) * | 2023-02-03 | 2024-08-07 | Aptiv Technologies AG | Data structure for efficient training of semantic segmentation models |
| KR102870700B1 (en) * | 2023-10-26 | 2025-10-14 | 주식회사 스카이오토넷 | Cms camera device in large commercial vehicles and method for operating thereof |
| US12322162B1 (en) * | 2024-05-09 | 2025-06-03 | Geotab Inc. | Systems and methods for training vehicle collision and near-miss detection models |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130322691A1 (en) * | 2012-06-01 | 2013-12-05 | Ricoh Company, Ltd. | Target recognition system and target recognition method executed by the target recognition system |
| US8874267B1 (en) * | 2012-06-20 | 2014-10-28 | Google Inc. | Avoiding blind spots of other vehicles |
| US20160054452A1 (en) * | 2014-08-20 | 2016-02-25 | Nec Laboratories America, Inc. | System and Method for Detecting Objects Obstructing a Driver's View of a Road |
| US20160125249A1 (en) * | 2014-10-30 | 2016-05-05 | Toyota Motor Engineering & Manufacturing North America, Inc. | Blur object tracker using group lasso method and apparatus |
| WO2017027030A1 (en) * | 2015-08-12 | 2017-02-16 | Hewlett Packard Enterprise Development Lp | Retraining a machine classifier based on audited issue data |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8842163B2 (en) | 2011-06-07 | 2014-09-23 | International Business Machines Corporation | Estimation of object properties in 3D world |
| US9466215B2 (en) * | 2012-03-26 | 2016-10-11 | Robert Bosch Gmbh | Multi-surface model-based tracking |
| US9070202B2 (en) | 2013-03-14 | 2015-06-30 | Nec Laboratories America, Inc. | Moving object localization in 3D using a single camera |
| US9396553B2 (en) | 2014-04-16 | 2016-07-19 | Xerox Corporation | Vehicle dimension estimation from vehicle images |
| US10410096B2 (en) | 2015-07-09 | 2019-09-10 | Qualcomm Incorporated | Context-based priors for object detection in images |
| US10029622B2 (en) | 2015-07-23 | 2018-07-24 | International Business Machines Corporation | Self-calibration of a static camera from vehicle information |
| US10424064B2 (en) * | 2016-10-18 | 2019-09-24 | Adobe Inc. | Instance-level semantic segmentation system |
-
2018
- 2018-03-29 KR KR1020197029041A patent/KR102629651B1/en active Active
- 2018-03-29 CN CN201880036861.1A patent/CN110678872A/en active Pending
- 2018-03-29 US US16/500,155 patent/US11216673B2/en active Active
- 2018-03-29 WO PCT/EP2018/058033 patent/WO2018184963A2/en not_active Ceased
- 2018-03-29 EP EP18714245.0A patent/EP3607489B1/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130322691A1 (en) * | 2012-06-01 | 2013-12-05 | Ricoh Company, Ltd. | Target recognition system and target recognition method executed by the target recognition system |
| US8874267B1 (en) * | 2012-06-20 | 2014-10-28 | Google Inc. | Avoiding blind spots of other vehicles |
| US20160054452A1 (en) * | 2014-08-20 | 2016-02-25 | Nec Laboratories America, Inc. | System and Method for Detecting Objects Obstructing a Driver's View of a Road |
| US20160125249A1 (en) * | 2014-10-30 | 2016-05-05 | Toyota Motor Engineering & Manufacturing North America, Inc. | Blur object tracker using group lasso method and apparatus |
| WO2017027030A1 (en) * | 2015-08-12 | 2017-02-16 | Hewlett Packard Enterprise Development Lp | Retraining a machine classifier based on audited issue data |
Non-Patent Citations (4)
| Title |
|---|
| ANONYMOUS: "Direct manipulation interface", 27 December 2016 (2016-12-27), XP002781196, Retrieved from the Internet <URL:https://en.wikipedia.org/w/index.php?title=Direct_manipulation_interface&oldid=756947614> [retrieved on 20180518] * |
| ARSALAN MOUSAVIAN ET AL: "3D Bounding Box Estimation Using Deep Learning and Geometry", ARXIV.ORG, 1 December 2016 (2016-12-01), pages 1 - 9, XP055474731, Retrieved from the Internet <URL:https://arxiv.org/pdf/1612.00496v1.pdf> [retrieved on 20180514] * |
| BO LI: "3D Fully Convolutional Network for Vehicle Detection in Point Cloud", 16 January 2017 (2017-01-16), XP055474739, Retrieved from the Internet <URL:https://arxiv.org/pdf/1611.08069.pdf> [retrieved on 20180514] * |
| XIAOZHI CHEN ET AL: "Multi-view 3D Object Detection Network for Autonomous Driving", ARXIV.ORG, 23 November 2016 (2016-11-23), XP055474745, Retrieved from the Internet <URL:https://arxiv.org/pdf/1611.07759v1.pdf> [retrieved on 20180514] * |
Also Published As
| Publication number | Publication date |
|---|---|
| EP3607489A2 (en) | 2020-02-12 |
| KR20190132404A (en) | 2019-11-27 |
| US20200349365A1 (en) | 2020-11-05 |
| US11216673B2 (en) | 2022-01-04 |
| WO2018184963A2 (en) | 2018-10-11 |
| CN110678872A (en) | 2020-01-10 |
| KR102629651B1 (en) | 2024-01-29 |
| EP3607489B1 (en) | 2023-05-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2018184963A3 (en) | Direct vehicle detection as 3d bounding boxes using neural network image processing | |
| CA2975139C (en) | Stereo camera system for collision avoidance during aircraft surface operations | |
| CN106462996B (en) | Method and device for displaying vehicle surrounding environment without distortion | |
| US9102269B2 (en) | Field of view matching video display system | |
| WO2020146491A3 (en) | Using light detection and ranging (lidar) to train camera and imaging radar deep learning networks | |
| WO2020056431A8 (en) | System and method for three-dimensional (3d) object detection | |
| CA3027899C (en) | Ground plane detection for placement of augmented reality objects | |
| EP4266085A3 (en) | Fusion-based object tracker using lidar point cloud and surrounding cameras for autonomous vehicles | |
| EP3444748A3 (en) | Automated detection and avoidance system | |
| EP3293488A3 (en) | System and method of simulataneously generating a multiple lane map and localizing a vehicle in the generated map | |
| CN105718853B (en) | Obstacle detection device and obstacle detection method | |
| KR20190095592A (en) | Method and Apparatus for Vehicle Detection Using Lidar Sensor and Camera | |
| MY165967A (en) | Moving-body-detecting device and moving-body-detecting system | |
| CA3155737A1 (en) | Systems and methods for providing for the processing of objects in vehicles | |
| WO2015177643A3 (en) | Systems and methods for braking a vehicle based on a detected object | |
| EP2937757A3 (en) | Methods and systems for object detection using multiple sensors | |
| EP3103695A3 (en) | Driver assistance apparatus and control method for the same | |
| MX2017012837A (en) | Rear obstacle detection and distance estimation. | |
| EP3163506A1 (en) | Method for stereo map generation with novel optical resolutions | |
| JP2016530639A5 (en) | ||
| MX350354B (en) | Three-dimensional object detection device. | |
| EP4420930A3 (en) | Image processing device, image processing method, and image processing system | |
| WO2017158167A3 (en) | A computer implemented method and systems for tracking an object in a 3d scene | |
| WO2004028169A3 (en) | Stereo night vision system for vehicles | |
| MX344875B (en) | Three-dimensional object detection device. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 18714245 Country of ref document: EP Kind code of ref document: A2 |
|
| ENP | Entry into the national phase |
Ref document number: 20197029041 Country of ref document: KR Kind code of ref document: A |
|
| NENP | Non-entry into the national phase |
Ref country code: DE |
|
| ENP | Entry into the national phase |
Ref document number: 2018714245 Country of ref document: EP Effective date: 20191104 |